关键词: 大数据, 整合分析, 变量选择, 医疗支出, 癌症基因

Abstract: The difference of data source, high dimensionality and sparsity are the main characteristics of big data. How to mining the heterogeneity and association of different datasets and achieve dimension reduction is one of goals and challenges of big data analysis. Integrative analysis provides an effective way of analyzing big data. It simultaneously analyzes multiple datasets, avoiding the model instability from individual variations caused by regional and time factor and so on. The coefficients of each covariate across all datasets are treated as a group and penalty function is used to shrinkage these groups of coefficients to achieve variable selection. In this paper, we review the existing research of penalized integrative analysis from three aspects of homogeneity integrative analysis, heterogeneity integrative analysis and network integrative analysis. Three simulations are conducted to verify the performance of integrative analysis, including weak, moderate and strong correlations. It shows that Group Bridge、 Group MCP、Composite MCP perform well, and Group Bridge has the lowest false positive and is most stable. Finally, integrative analysis is applied to analyze the new rural cooperative medical expenditure data with source difference, as well as cancer genetics data with typical characteristics of big data such as super high dimension and small sample.

Key words: Big Data, Integrative Analysis, Variable Selection, Medical Expenditure, Cancer Genetics Data